A crash prediction computing system includes a machine learning module capable of analyzing data logs associated with each of a plurality of services or applications to identify and categorize every error, exception, and/or crash, such as those resulting from client system interactions based on crash type, customer profile type, customer screen navigation flow, time or crash. The machine learning algorithms continuously train the crash prediction models for each crash category with associated client computing system navigation flow. The crash prediction computing system applies each model before each screen/activity navigation to predict whether the next move will result in an error, exception or crash, and for each predicted error, exception, or crash, automatically implement alternate route functionality to arrive at a desired target.

In one embodiment, a method for margin determination for a computing system with a real time operating system and priority preemptive scheduling comprises: scheduling a set of tasks to be executed in one or more partitions, wherein each is assigned a priority, wherein the tasks comprise periodic and/or aperiodic tasks; executing the set of tasks on the computing system within the scheduled periodic time window; introducing an overhead task executed for an execution duration controlled either by the real time operating system or by the overhead task; controlling the overhead task to converge on a point of failure at which a length of the execution duration of the overhead task causes either: 1) a periodic task to fail to execute within a deadline, or 2) time available for the aperiodic tasks to execute to fall below a threshold; and defining a partition margin corresponding to the point of failure.

Methods, apparatuses, and systems related to a memory device are described. A controller may be configured to predict a temperature of a memory based on a real-time temperature of the controller. Based on the predicted temperature of the memory, the controller may execute a remedial action to reduce an actual temperature of the memory for executing an upcoming operation.

Methods, systems, and devices for hazard detection in a multi-memory device are described. A device may receive a first command that indicates a first bank address, a first row address, and a first column address. Based on the first bank address, the device may select a buffer for a hazard detection procedure that detects hazardous commands. The device may compare, as part of the hazard detection procedure, the first row address and the first column address from the first command with a second row address and a second column address from a second command in the buffer. The device may determine whether the first command and the second command are hazardous commands based on comparing the first row address and the first column address from the first command with the second row address and the second column address from the second command.

Methods and articles of manufacture for hosting a safety critical application on an uncontrolled data processing device are provided. Various combinations of installation, functional, host integrity, coexistence, interoperability, power management, and environment checks are performed at various times to determine if the safety critical application operates properly on the device. The operation of the SCA on the UDPD may be controlled accordingly.

A customer of a policy management service may use an interface with a configuration and management service to interact with policies that may be applicable to the customer's one or more resources. The customer may create and/or modify the policies and the configuration and management service may notify one or more other entities of the created and/or modified policies. The one or more other entities may be operated by user authorized to approve the created and/or modified policies. Interactions with the configuration and management service may be the same as the interactions with the policy management service.

Respective life expectancies of a first data unit and a second data unit of the memory device is obtained. A first initial age value corresponding to the first data unit and a second initial age value corresponding to the second data unit are determined. A lower one of the first initial age value and the second initial age value is identified. A first media management operation on a corresponding one of the first data unit or the second data unit associated with the lower one of the first initial age value and the second initial age value is performed. A second media management operation on the first data unit and the second data unit is performed.

A memory management method is provided according to the invention. The method includes: reading a physical unit and updating a read count of the physical unit; scanning the physical unit if the updated read count is not less than a read count threshold; and adjusting the read count threshold according to the read count and a read error bit. Therefore, a data unit that needs to be scanned can be determined to reduce unnecessary data scanning.

A method of implementing an automated technology for conducting functional safety (FuSa) diagnostic coverage is provided. The method can include receiving functional safety information that includes failure modes defining wrong values of a signal indicating a factor manifesting an error, receiving an identification of internal safety protected signals and a diagnostic coverage for the FuSa block, performing back tracing of possible paths for an output port of a FuSa block for each failure mode of each safety protected signal, determining an area for each possible path, and determining, based on a diagnostic coverage and area calculated for each of the paths, a diagnostic coverage for each failure mode of the FuSa block.

In one aspect, a fault intercepting computing device is provided. The fault intercepting computing devices receives a request message including an outdated key value from a client computing device, retrieves an updated key value from a key mapping database, generates a remapped request based on the request message by replacing the outdated key value with the updated key value in the request message, and transmits the remapped request to a host computing device. The fault intercepting computing device further receives an initial response from the host computing device including the updated key value, generates a surrogate response based on the initial response by replacing the updated key value with the outdated key value, appends a key remapped flag to the surrogate response, transmits the surrogate response, including the outdated key value to the client computing device.